Attenuation-corrected Images Research Articles

Impact of CT attenuation correction on viable myocardium detection in combined SPECT and PET/CT: A retrospective cohort study.

The influence of computed tomography attenuation correction (CTAC) on the accuracy of diagnosing viable myocardium using Tc-99m-MIBI dedicated cardiac cadmium-zinc-telluride (CZT) single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) combined with F-18-FDG Positron Emission Tomography/Computed Tomography (PET/CT) metabolic imaging, compared with conventional SPECT MPI, remains to be fully elucidated. To evaluate the impact of CTAC on the accuracy of diagnosing viable myocardium using Tc-99m-MIBI dedicated cardiac CZT SPECT MPI combined with F-18-FDG PET/CT, compared to conventional SPECT MPI. 193 patients underwent CZT SPECT and F-18-FDG PET/CT imaging, while 39 patients underwent conventional SPECT and F-18-FDG PET/CT imaging, with both groups utilizing CT for attenuation correction. The injured myocardium (hibernating and scarring) was quantified using the Q.PET software. After CTAC, both groups showed significant improvements in perfusion of the injured myocardial areas, particularly in the inferior wall (INF). The reduction in perfusion was more notable in the CZT SPECT group than that in the conventional group, particularly in the inferior and lateral walls. Among patients with large cardiac chambers, those undergoing MPI with CZT, with normal weights, or males, hibernating myocardium (HM) and scar post-CTAC reductions were particularly significant in the INF. If HM ≥ 10% is considered an indicator for recommended revascularization, among the 87 patients without prior cardiac bypass, 25 (28.7%) might not require revascularization treatment. Dedicated cardiac CZT SPECT and conventional SPECT MPI combined with F-18-FDG PET/CT significantly influenced the assessment of viable myocardium. The impact of CTAC was more profound in dedicated cardiac CZT SPECT, particularly in the INF region. CTAC significantly enhances the accuracy of viable myocardial assessment and may influence clinical decisions regarding revascularization therapy. Therefore, CTAC should be routinely used in dedicated cardiac CZT SPECT MPI combined with F-18-FDG PET/CT for myocardial viability diagnosis.

Study of Attenuation Correction Using a Cardiac Dynamic Phantom: Synchronized Time-Phase-Gated Attenuation Correction Method.

In cardiac nuclear medicine examinations, absorption in the body is the main factor in the degradation of the image quality. The Chang and external source methods were used to correct for absorption in the body. However, fundamental studies on attenuation correction for electrocardiogram (ECG)-synchronized CT imaging have not been performed. Therefore, we developed and improved an ECG-synchronized cardiac dynamic phantom and investigated the synchronized time-phase-gated attenuation correction (STPGAC) method using ECG-synchronized SPECT and CT images of the same time phase. Methods: As a basic study, SPECT was performed using synchronized time-phase-gated (STPG) SPECT and non-phase-gated (NPG) SPECT. The attenuation-corrected images were, first, CT images with the same time phase as the ECG waveform of the gated SPECT acquisition (with CT images with the ECG waveform of the CT acquisition as the reference); second, CT images with asynchronous ECG; third, CT images of the 75% region; and fourth, CT images of the 40% region. Results: In the analysis of cardiac function in the phantom experiment, left ventricle ejection fraction (heart rate, 11.5%-13.4%; myocardial wall, 49.8%-55.7%) in the CT images was compared with that in the STPGAC method (heart rate, 11.5%-13.3%; myocardial wall, 49.6%-55.5%), which was closer in value to that of the STPGAC method. In the phantom polar map segment analyses, none of the images showed variability (F (10,10) < 0.5, P = 0.05). All images were correlated (r = 0.824-1.00). Conclusion: In this study, we investigated the STPGAC method using a SPECT/CT system. The STPGAC method showed similar values of cardiac function analysis to the CT images, suggesting that the STPGAC method accurately reconstructed the distribution of blood flow in the myocardial region. However, the target area for attenuation correction of the heart region was smaller than that of the whole body, and changing the gated SPECT conditions and attenuation-corrected images did not affect myocardial blood flow analysis.

AI-Defined Cardiac Anatomy Improves Risk Stratification of Hybrid Perfusion Imaging

BackgroundComputed tomography attenuation correction (CTAC) improves perfusion quantification of hybrid myocardial perfusion imaging by correcting for attenuation artifacts. Artificial intelligence (AI) can automatically measure coronary artery calcium (CAC) from CTAC to improve risk prediction but could potentially derive additional anatomic features. ObjectivesThe authors evaluated AI-based derivation of cardiac anatomy from CTAC and assessed its added prognostic utility. MethodsThe authors considered consecutive patients without known coronary artery disease who underwent single-photon emission computed tomography/computed tomography (CT) myocardial perfusion imaging at 3 separate centers. Previously validated AI models were used to segment CAC and cardiac structures (left atrium, left ventricle, right atrium, right ventricular volume, and left ventricular [LV] mass) from CTAC. They evaluated associations with major adverse cardiovascular events (MACEs), which included death, myocardial infarction, unstable angina, or revascularization. ResultsIn total, 7,613 patients were included with a median age of 64 years. During a median follow-up of 2.4 years (IQR: 1.3-3.4 years), MACEs occurred in 1,045 (13.7%) patients. Fully automated AI processing took an average of 6.2 ± 0.2 seconds for CAC and 15.8 ± 3.2 seconds for cardiac volumes and LV mass. Patients in the highest quartile of LV mass and left atrium, LV, right atrium, and right ventricular volume were at significantly increased risk of MACEs compared to patients in the lowest quartile, with HR ranging from 1.46 to 3.31. The addition of all CT-based volumes and CT-based LV mass improved the continuous net reclassification index by 23.1%. ConclusionsAI can automatically derive LV mass and cardiac chamber volumes from CT attenuation imaging, significantly improving cardiovascular risk assessment for hybrid perfusion imaging.

Prospective Study to Evaluate the Role of Dual Point Contrast-enhanced Magnetic Resonance Imaging in Differentiation of Brain Tumoral from Nontumoral Tissue: A Magnetic Resonance/PET Study.

Follow-up imaging of gliomas is crucial to look for residual or recurrence and to differentiate them from nontumoral tissue. Positron emission tomography (PET)-magnetic resonance imaging (MRI) is the problem-solving tool in such cases. We investigated the role of dual point contrast (DPC)-enhanced MRI to discriminate tumoral from the nontumoral tissue compared to PET-MRI taken as the gold standard. The institutional ethics committee approved the study, and consent was obtained from all the patients included in the study. We prospectively did immediate and 75-min delayed contrast MRI in glioma cases who came for follow-up as a part of PET-MRI study in our institute. Subtracted images were obtained using immediate and 75-min delayed contrast images. Color-coded subtracted images were compared with PET-MRI images. 75-min delayed contrast MRI and diffusion-weighted imaging (DWI) images with Gray Scale inversion were compared with PET attenuation-corrected images. We included 23 PET MRI cases done with different radiotracers in our study. Overall, we found PET-DPC correlation in (20/20 ~ 100%) cases of enhancing tumors. In two cases (DOPA and fluorodeoxyglucose), since they were nonenhancing low-grade gliomas and the other one was melanoma with intrinsic T1 hyperintensity and the DPC technique could not be used. DWI-PET correlated in 17/19 (~89.4%) cases, and perfusion-weighted imaging (PWI)-PET dynamic susceptibility contrast (DSC)/ASL correlated in 14/18 (~77.7%) cases after cases with hemorrhage were excluded. DPC MRI showed a good correlation with PET MRI in discriminating tumoral from the nontumoral tissue. DPC MRI can act as a potential alternative to PET MRI in peripheral hospitals where PET is not available. However, the DPC technique is limited in low-grade nonenhancing gliomas.

Clinical Feasibility of Deep Learning-Based Attenuation Correction Models for Tl-201 Myocardial Perfusion SPECT.

We aimed to develop deep learning (DL)-based attenuation correction models for Tl-201 myocardial perfusion SPECT (MPS) images and evaluate their clinical feasibility. We conducted a retrospective study of patients with suspected or known coronary artery disease. We proposed a DL-based image-to-image translation technique to transform non-attenuation-corrected images into CT-based attenuation-corrected (CT AC ) images. The model was trained using a modified U-Net with structural similarity index (SSIM) loss and mean squared error (MSE) loss and compared with other models. Segment-wise analysis using a polar map and visual assessment for the generated attenuation-corrected (GEN AC ) images were also performed to evaluate clinical feasibility. This study comprised 657 men and 328 women (age, 65 ± 11 years). Among the various models, the modified U-Net achieved the highest performance with an average mean absolute error of 0.003, an SSIM of 0.990, and a peak signal-to-noise ratio of 33.658. The performance of the model was not different between the stress and rest datasets. In the segment-wise analysis, the myocardial perfusion of the inferior wall was significantly higher in GEN AC images than in the non-attenuation-corrected images in both the rest and stress test sets ( P < 0.05). In the visual assessment of patients with diaphragmatic attenuation, scores of 4 (similar to CT AC images) or 5 (indistinguishable from CT AC images) were assigned to most GEN AC images (65/68). Our clinically feasible DL-based attenuation correction models can replace the CT-based method in Tl-201 MPS, and it would be useful in case SPECT/CT is unavailable for MPS.

Feasibility of direct brain 18F-fluorodeoxyglucose-positron emission tomography attenuation and high-resolution correction methods using deep learning.

To develop the following three attenuation correction (AC) methods for brain 18F-fluorodeoxyglucose-positron emission tomography (PET), using deep learning, and to ascertain their precision levels: (i) indirect method; (ii) direct method; and (iii) direct and high-resolution correction (direct+HRC) method. We included 53 patients who underwent cranial magnetic resonance imaging (MRI) and computed tomography (CT) and 27 patients who underwent cranial MRI, CT, and PET. After fusion of the magnetic resonance, CT, and PET images, resampling was performed to standardize the field of view and matrix size and prepare the data set. In the indirect method, synthetic CT (SCT) images were generated, whereas in the direct and direct+HRC methods, a U-net structure was used to generate AC images. In the indirect method, attenuation correction was performed using SCT images generated from MRI findings using U-net instead of CT images. In the direct and direct+HRC methods, AC images were generated directly from non-AC images using U-net, followed by image evaluation. The precision levels of AC images generated using the indirect and direct methods were compared based on the normalized mean squared error (NMSE) and structural similarity (SSIM). Visual inspection revealed no difference between the AC images prepared using CT-based attenuation correction and those prepared using the three methods. The NMSE increased in the order indirect, direct, and direct+HRC methods, with values of 0.281×10-3, 4.62×10-3, and 12.7×10-3, respectively. Moreover, the SSIM of the direct+HRC method was 0.975. The direct+HRC method enables accurate attenuation without CT exposure and high-resolution correction without dedicated correction programs.

Application of geometric shape-based CT field-of-view extension algorithms in an all-digital positron emission tomography/computed tomography system.

Computed tomography (CT)-based positron emission tomography (PET) attenuation correction (AC) is a commonly used method in PET AC. However, the CT truncation caused by the subject's limbs outside the CT field-of-view (FOV) leads to errors in PET AC. In order to enhance the quantitative accuracy of PET imaging in the all-digital DigitMI 930 PET/CT system, we assessed the impact of FOV truncation on its image quality and investigated the effectiveness of geometric shape-based FOV extension algorithms in this system. We implemented two geometric shape-based FOV extension algorithms. By setting the data from different numbers of detector channels on either side of the sinogram to zero, we simulated various levels of truncation. Specific regions of interest (ROI) were selected, and the mean values of these ROIs were calculated to visually compare the differences between truncated CT, CT extended using the FOV extension algorithms, and the original CT. Furthermore, we conducted statistical analyses on the mean and standard deviation of residual maps between truncated/extended CT and the original CT at different levels of truncation. Subsequently, similar data processing was applied to PET images corrected using original CT and those corrected using simulated truncated and extended CT images. This allowed us to evaluate the influence of FOV truncation on the images produced by the DigitMI 930 PET/CT system and assess the effectiveness of the FOV extension algorithms. Truncation caused bright artifacts at the CT FOV edge and a slight increase in pixel values within the FOV. When using truncated CT data for PET AC, the PET activity outside the CT FOV decreased, while the extension algorithm effectively reduced these effects. Patient data showed that the activity within the CT FOV decreased by 60% in the truncated image compared to the base image, but this number could be reduced to at least 17.3% after extension. The two geometric shape-based algorithms effectively eliminate CT truncation artifacts and restore the true distribution of CT shape and PET emission data outside the FOV in the all-digital DigitMI 930 PET/CT system. These two algorithms can be used as basic solutions for CT FOV extension in all-digital PET/CT systems.

Comparison of the prognostic value between quantification and visual estimation of coronary calcification from attenuation CT in patients undergoing SPECT myocardial perfusion imaging.

We investigated the prognostic utility of visually estimated coronary artery calcification (VECAC) from low dose computed tomography attenuation correction (CTAC) scans obtained during SPECT/CT myocardial perfusion imaging (MPI), and assessed how it compares to coronary artery calcifications (CAC) quantified by calcium score on CTACs (QCAC). From the REFINE SPECT Registry 4,236 patients without prior coronary stenting with SPECT/CT performed at a single center were included (age: 64 ± 12 years, 47% female). VECAC in each coronary artery (left main, left anterior descending, circumflex, and right) were scored separately as 0 (absent), 1 (mild), 2 (moderate), or 3 (severe), yielding a possible score of 0-12 for each patient (overall VECAC grade zero:0, mild:1-2, moderate: 3-5, severe: >5). CAC scoring of CTACs was performed at the REFINE SPECT core lab with dedicated software. VECAC was correlated with categorized QCAC (zero: 0, mild: 1-99, moderate: 100-399, severe: ≥400). A high degree of correlation was observed between VECAC and QCAC, with 73% of VECACs in the same category as QCAC and 98% within one category. There was substantial agreement between VECAC and QCAC (weighted kappa: 0.78 with 95% confidence interval: 0.76-0.79, p < 0.001). During a median follow-up of 25 months, 372 patients (9%) experienced major adverse cardiovascular events (MACE). In survival analysis, both VECAC and QCAC were associated with MACE. The area under the receiver operating characteristic curve for 2-year-MACE was similar for VECAC when compared to QCAC (0.694 versus 0.691, p = 0.70). In conclusion, visual assessment of CAC on low-dose CTAC scans provides good estimation of QCAC in patients undergoing SPECT/CT MPI. Visually assessed CAC has similar prognostic value for MACE in comparison to QCAC.

Evaluation of MR anatomically-guided PET reconstruction using a convolutional neural network in PSMA patients.

&#xD;Recently, approaches have utilized the superior anatomical information provided by magnetic resonance imaging (MRI) to guide the reconstruction of positron emission tomography (PET). One of those approaches is the Bowsher's prior, which has been accelerated lately with a convolutional neural network (CNN) to reconstruct MR-guided PET in the imaging domain in routine clinical imaging. Two differently trained Bowsher-CNN methods (B-CNN0 and B-CNN) have been trained and tested on brain PET/MR images on non-PSMA tracers, but so far, have not been evaluated in other anatomical regions yet. &#xD;Methods: &#xD;A NEMA phantom with five of its six spheres filled with the same, calibrated concentration of 18F-DCFPyL-PSMA, and thirty-two patients (mean age 64 ± 7 years) with biopsy-confirmed PCa were used in this study. Reconstruction with either of the two available Bowsher-CNN methods were performed on the conventional MR-based attenuation correction (MRAC) and T1-MR images in the imaging domain. Detectable volume of the spheres and tumors, relative contrast recovery (CR), and background variation (BV) were measured for the MRAC and the Bowsher-CNN images, and qualitative assessment was conducted by ranking the image sharpness and quality by two experienced readers.&#xD;Results: &#xD;For the phantom study, the B-CNN produced 12.7% better CR compared to conventional reconstruction. The small sphere volume (<1.8mL) detectability improved from MRAC to B-CNN by nearly 13%, while measured activity was higher than the ground-truth by 8%. The signal-to-noise ratio (SNR), CR, and BV were significantly improved (p<0.05) in B-CNN images of the tumor. The qualitative analysis determined that tumor sharpness was excellent in 76% of the PET images reconstructed with the B-CNN method, compared to conventional reconstruction. &#xD;Conclusions: &#xD;Applying the MR-guided B-CNN in clinical prostate PET/MR imaging improves some quantitative, as well as qualitative imaging measures. The measured improvements in the phantom are also clearly translated into clinical application.

Clinical Utility of CT-based Attenuation-correction in Myocardial Perfusion SPECT Imaging.

We aimed to investigate and compare the role of computed tomography (CT)-based attenuation-corrected images (AC) with non-attenuation-corrected images (NAC) obtained by single-photon emission computed tomography/computed tomography (SPECT/CT) myocardial perfusion imaging (MPI). The data of 124 patients who were applied one-day stress-rest Tc-99m sestamibi SPECT/CT MPI and who had coronary angiography (CAG) results within ±3 months were retrospectively reviewed. AC and NAC images were visually evaluated by two nuclear medicine specialists in a consensus. CAG results were used as the reference standard. Specificity, sensitivity, and accuracy were calculated as 66%, 61%, 71%, 79% and 69%, 70% for AC and NAC imaging in the whole group, respectively. There was no statistically significant difference between AC and NAC images for specificity, sensitivity, and accuracy in the male and female subgroups. In the diagnosis of right coronary artery (RCA) disease, CT AC significantly increased the specificity from 87% to 96%. However, in the left anterior descending artery (LAD) region, the specificity was significantly reduced from 95% to 77%. CT-based AC did not significantly contribute to diagnostic performance for increased specificity for the RCA and reduced specificity in the LAD region. Therefore, AC images should always be evaluated side by side with NAC images to benefit from the different advantages of both techniques.

Artificial Intelligence in Nuclear Cardiology

Artificial intelligence (AI) encompasses a variety of computer algorithms that have a wide range of potential clinical applications in nuclear cardiology. This article will introduce core terminology and concepts for AI including classifications of AI as well as training and testing regimens. We will then highlight the potential role for AI to improve image registration and image quality. Next, we will discuss methods for AI-driven image attenuation correction. Finally, we will review advancements in machine learning and deep-learning applications for disease diagnosis and risk stratification, including efforts to improve clinical translation of this valuable technology with explainable AI models.

Deep learning-based attenuation map generation with simultaneously reconstructed PET activity and attenuation and low-dose application

Objective. In PET/CT imaging, CT is used for positron emission tomography (PET) attenuation correction (AC). CT artifacts or misalignment between PET and CT can cause AC artifacts and quantification errors in PET. Simultaneous reconstruction (MLAA) of PET activity (λ-MLAA) and attenuation (μ-MLAA) maps was proposed to solve those issues using the time-of-flight PET raw data only. However, λ-MLAA still suffers from quantification error as compared to reconstruction using the gold-standard CT-based attenuation map (μ-CT). Recently, a deep learning (DL)-based framework was proposed to improve MLAA by predicting μ-DL from λ-MLAA and μ-MLAA using an image domain loss function (IM-loss). However, IM-loss does not directly measure the AC errors according to the PET attenuation physics. Our preliminary studies showed that an additional physics-based loss function can lead to more accurate PET AC. The main objective of this study is to optimize the attenuation map generation framework for clinical full-dose 18F-FDG studies. We also investigate the effectiveness of the optimized network on predicting attenuation maps for synthetic low-dose oncological PET studies. Approach. We optimized the proposed DL framework by applying different preprocessing steps and hyperparameter optimization, including patch size, weights of the loss terms and number of angles in the projection-domain loss term. The optimization was performed based on 100 skull-to-toe 18F-FDG PET/CT scans with minimal misalignment. The optimized framework was further evaluated on 85 clinical full-dose neck-to-thigh 18F-FDG cancer datasets as well as synthetic low-dose studies with only 10% of the full-dose raw data. Main results. Clinical evaluation of tumor quantification as well as physics-based figure-of-merit metric evaluation validated the promising performance of our proposed method. For both full-dose and low-dose studies, the proposed framework achieved <1% error in tumor standardized uptake value measures. Significance. It is of great clinical interest to achieve CT-less PET reconstruction, especially for low-dose PET studies.

Deep Learning-Based Attenuation Correction Improves Diagnostic Accuracy of Cardiac SPECT.

To improve diagnostic accuracy, myocardial perfusion imaging (MPI) SPECT studies can use CT-based attenuation correction (AC). However, CT-based AC is not available for most SPECT systems in clinical use, increases radiation exposure, and is impacted by misregistration. We developed and externally validated a deep-learning model to generate simulated AC images directly from non-AC (NC) SPECT, without the need for CT. Methods: SPECT myocardial perfusion imaging was performed using 99mTc-sestamibi or 99mTc-tetrofosmin on contemporary scanners with solid-state detectors. We developed a conditional generative adversarial neural network that applies a deep learning model (DeepAC) to generate simulated AC SPECT images. The model was trained with short-axis NC and AC images performed at 1 site (n = 4,886) and was tested on patients from 2 separate external sites (n = 604). We assessed the diagnostic accuracy of the stress total perfusion deficit (TPD) obtained from NC, AC, and DeepAC images for obstructive coronary artery disease (CAD) with area under the receiver-operating-characteristic curve. We also quantified the direct count change among AC, NC, and DeepAC images on a per-voxel basis. Results: DeepAC could be obtained in less than 1 s from NC images; area under the receiver-operating-characteristic curve for obstructive CAD was higher for DeepAC TPD (0.79; 95% CI, 0.72-0.85) than for NC TPD (0.70; 95% CI, 0.63-0.78; P < 0.001) and similar to AC TPD (0.81; 95% CI, 0.75-0.87; P = 0.196). The normalcy rate in the low-likelihood-of-coronary-disease population was higher for DeepAC TPD (70.4%) and AC TPD (75.0%) than for NC TPD (54.6%, P < 0.001 for both). The positive count change (increase in counts) was significantly higher for AC versus NC (median, 9.4; interquartile range, 6.0-14.2; P < 0.001) than for AC versus DeepAC (median, 2.4; interquartile range, 1.3-4.2). Conclusion: In an independent external dataset, DeepAC provided improved diagnostic accuracy for obstructive CAD, as compared with NC images, and this accuracy was similar to that of actual AC. DeepAC simplifies the task of artifact identification for physicians, avoids misregistration artifacts, and can be performed rapidly without the need for CT hardware and additional acquisitions.

Inpainting the metal artifact region in MRI images by using generative adversarial networks with gated convolution.

Magnetic resonance imaging (MRI) plays an important role in clinical diagnosis, but it is susceptible to metal artifacts. The generative adversarial network GatedConv with gated convolution (GC) and contextual attention (CA) was used to inpaint the metal artifact region in MRI images. MRI images containing or near the teeth of 70 patients were collected, and the scanning sequence was a T1-weighted high-resolution isotropic volume examination sequence. A total of 10 000 slices were obtained after data enhancement, of which 8000 slices were used for training. MRI images were normalized to [-1,1]. Based on the randomly generated mask, U-Net, pix2pix, PConv with partial convolution, and GatedConv were used to inpaint the artifact region of MRI images. The mean absolute error (MAE) and peak signal-to-noise ratio (PSNR) for the mask were used to compare the results of these methods. The inpainting effect on the test dataset using dental masks was also evaluated. Besides, the artifact area of clinical MRI images was inpainted based on the mask sketched by physicians. Finally, the earring artifacts and artifacts caused by abnormal signal foci were inpainted to verify the generalization of the models. GatedConv could directly and effectively inpaint the incomplete MRI images generated by masks in the image domain. For the results of U-Net, pix2pix, PConv, and GatedConv, the masked MAEs were 0.1638, 0.1812, 0.1688, and 0.1596, respectively, and the masked PSNRs were 18.2136, 17.5692, 18.2258, and 18.3035dB, respectively. Using dental masks, the results of U-Net, pix2pix, and PConv differed more from the real images in terms of alveolar shape and surrounding tissue compared with GatedConv. GatedConv could inpaint the metal artifact region in clinical MRI images more effectively than the other models, but the increase in the mask area could reduce the inpainting effect. Inpainted MRI images by GatedConv and CT images with metal artifact reduction coincided with alveolar and tissue structure, and GatedConv could successfully inpaint artifacts caused by abnormal signal foci, whereas the other models failed. The ablation study demonstrated that GC and CA increased the reliability of the inpainting performance of GatedConv. MRI images are affected by metal, and signal void areas appear near metal. GatedConv can inpaint the MRI metal artifact region in the image domain directly and effectively and improve image quality. Medical image inpainting by GatedConv has potential value for tasks, such as positron emission tomography (PET) attenuation correction in PET/MRI and adaptive radiotherapy of synthetic CT based on MRI.

DeTransUnet: attenuation correction of gated cardiac images without structural information

Objective. Myocardial perfusion imaging (MPI) with positron emission tomography (PET) is a non-invasive imaging method, and it is of great significance to the diagnosis and prognosis of coronary heart disease. Attenuation correction (AC) for PET images is a necessary step for further quantitative analysis. In order not to use magnetic resonance (MR) or computed tomography (CT) images for AC, this work proposes DeTransUnet to obtain AC PET images directly from no-attenuation corrected (NAC) PET images. Approach. The proposed DeTransUnet is a 3D structure which combines the multi-scale deformable transformer layers and the 3D convolutional neural network (CNN). And it integrates the advantages of transformer with long-range dependence and CNN suitable for image calculation. The AC images using CT images for AC and scatter correction (SC) and are considered as training labels, while the NAC images are reconstructed without AC and SC. Standard uptake value (SUV) values are calculated for both NAC and AC images to exclude the influence of weight and injection dose. With NAC SUV images as the inputs of the DeTransUnet, the outputs of DeTransUnet are AC SUV images. Main results. The proposed DeTransUnet was performed on an MPI gated-PET dataset, and the results were compared with Unet2D and Unet2.5D. The metrics of the whole image and the left ventricular myocardium show that the proposed method has advantages over other deep learning methods. Significance. The proposed DeTransUnet is a novel AC framework that does not require CT or MR images. It can be used as an independent AC method on PET/MR instrument. In addition, when CT images contain defects or cannot be registered with PET images on PET/CT instrument, DeTransUnet is able to repair the defects and keep consistent with the NAC images.

Prevalence and clinical significance of incidental findings on CT attenuation correction for myocardial perfusion imaging

BackgroundThe appropriate clinical approach to incidentally detected lesions (IDLs) on CT attenuation correction (CTAC) images in myocardial perfusion imaging (MPI) remains uncertain. We sought to establish their prevalence and clinical significance in a large cohort and compared to previous studies to help provide further clarity and guide future clinical practice. Methods and ResultsA total of 3758 MPI studies were reviewed retrospectively. IDLs of potential clinical significance—not known before MPI - were reported in 245 (6.5%) of these cases. Following appropriate further investigation/follow-up, these were of proven clinical significance in 30 (12.2%) cases with 14 patients (5.7%) harboring previously undiagnosed or progressive malignancies. The positive predictive value (PPV) for clinically significant incidental findings on CTAC images was 17.2% and the PPV value for incidental malignant findings was 8.0%. ConclusionAlthough incidental findings on CTAC images in MPI are common and often clearly insignificant at time of MPI reporting, many are clinically significant with a relatively high positive predictive value. This is especially so for malignancies. Our findings, therefore, in combination with previous studies as described here support routine reporting and appropriate further investigation of incidental CTAC findings in MPI.

Deep learning with multiresolution handcrafted features for brain MRI segmentation

The segmentation of magnetic resonance (MR) images is a crucial task for creating pseudo computed tomography (CT) images which are used to achieve positron emission tomography (PET) attenuation correction. One of the main challenges of creating pseudo CT images is the difficulty to obtain an accurate segmentation of the bone tissue in brain MR images. Deep convolutional neural networks (CNNs) have been widely and efficiently applied to perform MR image segmentation. The aim of this work is to propose a segmentation approach that combines multiresolution handcrafted features with CNN-based features to add directional properties and enrich the set of features to perform segmentation. The main objective is to efficiently segment the brain into three tissue classes: bone, soft tissue, and air. The proposed method combines non subsampled Contourlet (NSCT) and non subsampled Shearlet (NSST) coefficients with CNN’s features using different mechanisms. The entropy value is calculated to select the most useful coefficients and reduce the input’s dimensionality. The segmentation results are evaluated using fifty clinical brain MR and CT images by calculating the precision, recall, dice similarity coefficient (DSC), and Jaccard similarity coefficient (JSC). The results are also compared to other methods reported in the literature. The DSC of the bone class is improved from 0.6179 ± 0.0006 to 0.6416 ± 0.0006. The addition of multiresolution features of NSCT and NSST with CNN’s features demonstrates promising results. Moreover, NSST coefficients provide more useful information than NSCT coefficients.

Reproducibility of quantitative coronary calcium scoring from PET/CT attenuation maps: comparison to ECG-gated CT scans.

We sought to evaluate inter-scan and inter-reader agreement of coronary calcium (CAC) scores obtained from dedicated, ECG-gated CAC scans (standard CAC scan) and ultra-low-dose, ungated computed tomography attenuation correction (CTAC) scans obtained routinely during cardiac PET/CT imaging. From 2928 consecutive patients who underwent same-day 82Rb cardiac PET/CT and gated CAC scan in the same hybrid PET/CT scanning session, we have randomly selected 200 cases with no history of revascularization. Standard CAC scans and ungated CTAC scans were scored by two readers using quantitative clinical software. We assessed the agreement between readers and between two scan protocols in 5 CAC categories (0, 1-10, 11-100, 101-400, and > 400) using Cohen's Kappa and concordance. Median age of patients was 70 (inter-quartile range: 63-77), and 46% were male. The inter-scan concordance index and Cohen's Kappa for readers 1 and 2 were 0.69; 0.75 (0.69, 0.81) and 0.72; 0.8 (0.75, 0.85) respectively. The inter-reader concordance index and Cohen's Kappa (95% confidence interval [CI]) was higher for standard CAC scans: 0.9 and 0.92 (0.89, 0.96), respectively, vs. for CTAC scans: 0.83 and 0.85 (0.79, 0.9) for CTAC scans (p = 0.02 for difference in Kappa). Most discordant readings between two protocols occurred for scans with low extent of calcification (CAC score < 100). CAC can be quantitatively assessed on PET CTAC maps with good agreement with standard scans, however with limited sensitivity for small lesions. CAC scoring of CTAC can be performed routinely without modification of PET protocol and added radiation dose.

Comparison of the Diagnostic Performance of Myocardial Perfusion Scintigraphy with and Without Attenuation Correction.

Objectives:Myocardial perfusion scintigraphy (MPS) is an important diagnostic test for detecting of coronary artery stenosis (CAS); however, tissue attenuation can lead to a difference in accuracy. We evaluated the diagnostic accuracy of attenuation-corrected (AC) and non-attenuation-corrected (NC) MPS for the detection of CAS.Methods:We retrospectively recruited patients who underwent invasive coronary angiography within 10 months after Tc-99m sestamibi MPS. The AC and NC perfusion images were analyzed separately, and each myocardial segment was scored based on relative uptake from 0 to 4. The summed stress score (SSS), summed rest score (SRS), and summed difference score (SDS) were calculated. The diagnostic performances were analyzed using the area under the curve (AUC) of the receiver operating characteristic curve.Results:From 117 patients, significant coronary stenosis was present in 66 patients (56%). The SSS and SRS obtained from NC-images were higher than those from AC, supporting the presence of attenuation artifacts in NC images. The AUC of SSS and SDS were significantly higher than those of SRS in both AC- and NC-images, but no significant difference was found between the AUC of SSS, and those of SDS. The optimal cut-offs were >12 for AC-SSS, >15 for NC-SSS, >4 for AC-SDS and >3 for NC-SDS. There was no statistically significant difference in the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy among AC-SSS, NC-SSS, AC-SDS, and NC-SDS.Conclusion:NC-based Tc-99m-sestamibi MPS promised comparable accuracy to AC images by using different cut-off values for diagnosis.

The role of multimodality imaging in patients with heart failure with reduced and preserved ejection fraction.

The burden of clinical heart failure, both heart failure with a reduced ejection fraction (HFrEF) and with a preserved ejection fraction (HFpEF), continues to increase both nationally and globally. This review summarizes the expanding role of multimodality imaging techniques in the evaluation and management these patients. Echocardiographic assessment for heart failure continues to expand and should include a robust hemodynamic and strain assessment. Nuclear techniques have also continued to evolve and advances including computed tomography attenuation correction for single photon emission-computed tomography positron-emission tomography increase diagnostic accuracy as well as provide information such as myocardial blood flow and viability assessment. Computed tomography imaging, already well established in the assessment of coronary and valvular disease, has increasing utility in the characterization of myopathy, and cardiac magnetic resonance imaging (MRI) continues to expand its role in tissue characterization to a wider breadth of diseases, including right ventricular cardiomyopathy and left ventricle noncompaction. Although heart failure remains a clinical diagnosis based on history and examination, early imaging is critical for further assessment. Due to its widespread availability, affordability, and safety, transthoracic echocardiography has long been the mainstay tool for both initial evaluation as well as for periodic surveillance of heart failure patients, but advances in multimodality imaging are occurring at a rapid pace and promise to provide an increasing wealth of data to help manage such patients.